class_docstr: Stochastic Gradient Riemannian Langevin Dynamics.
  
  This class implements the optimizer described in the paper *Stochastic Gradient
  Riemannian Langevin Dynamics on the Probability Simplex*, available at
  https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf.
  
  Parameters
  ----------
  learning_rate : float, default 0.001
      The initial learning rate. If None, the optimization will use the
      learning rate from ``lr_scheduler``. If not None, it will overwrite
      the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
      is also None, then it will be set to 0.01 by default.
  use_fused_step : bool, default False
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False, step is called,
      otherwise, fused_step is called.
code: def create_state(self, index, weight):
      return None
  
file_path: mxnet/python/mxnet/optimizer/sgld.py
imports: from __future__ import absolute_import
  import math
  from ..ndarray import clip
  from ..random import normal
  from .optimizer import Optimizer, register
label_desc: Use image node_id 2 for calling the SGLD obj's underlying member method code with example usage: obj.create_state(index, weight) and returns: None